Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the burden of behavioral disclosure. It communicates the returned content concretely and signals a read-only, exploratory operation. It does not mention side effects or error behavior, but for a schema/columns tool these are low-risk and reasonably inferable.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences with no filler: the first lists the returned information, the second provides usage positioning. The essential 'call first' guidance is front-loaded and every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a zero-parameter schema-discovery tool with no output schema, the description is complete: it names the dataset, enumerates the returned information, and tells the agent when to call it. Nothing essential is missing for correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has zero parameters, so there is no parameter semantics to document. The description appropriately focuses on what the caller will learn rather than on inputs, matching the empty input schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states what the tool returns: columns, numeric indicators, row count, and provenance banner. It identifies the specific dataset ('Threewayly') and positions the tool as a schema-discovery call, though it does not explicitly contrast it with siblings like dataset_provenance or dataset_stats.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides clear usage guidance with 'Call this first to learn the schema,' telling the agent when in a sequence to invoke it. It does not explicitly list alternatives or exclusions, but the positional instruction is sufficient for a simple introspection tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.